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Availability of research datasets is keystone for health and life sciencestudy reproducibility and scientific progress. Due to the heterogeneity andcomplexity of these data, a main challenge to be overcome by research datamanagement systems is to provide users with the best answers for their searchqueries. In the context of the 2016 bioCADDIE Dataset Retrieval Challenge, weinvestigate a novel ranking pipeline to improve the search of datasets used inbiomedical experiments. Our system comprises a query expansion model based onword embeddings, a similarity measure algorithm that takes into considerationthe relevance of the query terms, and a dataset categorisation method thatboosts the rank of datasets matching query constraints. The system wasevaluated using a corpus with 800k datasets and 21 annotated user queries. Oursystem provides competitive results when compared to the other challengeparticipants. In the official run, it achieved the highest infAP among theparticipants, being +22.3% higher than the median infAP of the participant'sbest submissions. Overall, it is ranked at top 2 if an aggregated metric usingthe best official measures per participant is considered. The query expansionmethod showed positive impact on the system's performance increasing ourbaseline up to +5.0% and +3.4% for the infAP and infNDCG metrics, respectively.Our similarity measure algorithm seems to be robust, in particular compared toDivergence From Randomness framework, having smaller performance variationsunder different training conditions. Finally, the result categorization did nothave significant impact on the system's performance. We believe that oursolution could be used to enhance biomedical dataset management systems. Inparticular, the use of data driven query expansion methods could be analternative to the complexity of biomedical terminologies.

Improving average ranking precision in user searches for biomedical research datasets